Cardiac rhythm management system with arrhythmia prediction and prevention

ABSTRACT

A cardiac rhythm management system predicts when an arrhythmia will occur and in one embodiment invokes a therapy to prevent or reduce the consequences of the arrhythmia. A cardiac arrhythmia trigger/marker is detected from a patient, and based on the trigger/marker, the system estimates a probability of a cardiac arrhythmia occurring during a predetermined future time interval. The system provides a list of triggers/markers, for which detection values are recurrently obtained at various predetermined time intervals. Based on detection values and conditional probabilities associated with the triggers/markers, a probability estimate of a future arrhythmia is computed. An arrhythmia prevention therapy is selected and activated based on the probability estimate of the future arrhythmia.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. Pat. No. 09/411,345, filed onOct. 1, 1999, now Pat. No. 6,272,377 the specification of which isincorporated herein by reference. This application is also related to aco-pending and commonly assigned application entitled “CARDIAC RHYTHMMANAGEMENT SYSTEM WITH ARRHYTHMIA PREDICTION AND PREVENTION” Ser. No.09/850,537, filed on even date herewith, the specification of which isincorporated herein by reference.

TECHNICAL FIELD

This document relates generally to cardiac rhythm management systems andparticularly, but not by way of limitation, to a system providingprediction of a future arrhythmia and preventive therapy for avoiding ormitigating the predicted arrhythmia.

BACKGROUND

The human heart normally maintains its own well-ordered intrinsic rhythmthrough generation of stimuli by pacemaker tissue that results in a waveof depolarization that spreads through specialized conducting tissue andthen into and through the myocardium. The well-ordered propagation ofelectrical depolarizations through the heart causes coordinatedcontractions of the myocardium that results in the efficient pumping ofblood. In a normally functioning heart, stimuli are generated under theinfluence of various physiological regulatory mechanisms to cause theheart to beat at a rate that maintains cardiac output at a levelsufficient to meet the metabolic needs of the body. Abnormalities ofexcitable cardiac tissue, however, can lead to abnormalities of heartrhythm that are called arrhythmias. All arrhythmias stem from one of twocauses: abnormalities of impulse generation or abnormalities of impulsepropagation. Arrhythmias can cause the heart to beat too slowly(bradycardia, or a bradyarrhythmia) or too quickly (tachycardia, or atachyarrhythmia), either of which may cause hemodynamic compromise ordeath.

Drug therapy is often effective in preventing the development ofarrhythmias and in restoring normal heart rhythms once an arrhythmia hasoccurred. However, drug therapy is not always effective for treatingparticular arrhythmias, and drug therapy usually causes side-effectsthat may be intolerable in certain patients. For such patients, analternative mode of treatment is needed. One such alternative mode oftreatment includes the use of a cardiac rhythm management systemincorporated into an implantable device that delivers therapy to theheart in the form of electrical stimuli. Such implantable devicesinclude cardiac pacemakers that deliver timed sequences of low energyelectrical stimuli, called pacing pulses, to the heart, via anintravascular leadwire or catheter (referred to as a lead) having one ormore electrodes disposed in or about the heart. Heart contractions areinitiated in response to such pacing pulses (referred to as capturingthe heart). By properly timing the delivery of pacing pulses, the heartcan be induced to contract in proper rhythm, greatly improving itsefficiency as a pump. Pacemakers are often used to treat patients withbradycardia. Pacemakers are also capable of delivering paces to theheart in such a manner that the heart rate is slowed, a pacing modereferred to as anti-tachyarrhythmia pacing.

Cardiac rhythm management systems also includecardioverter/defibrillators (ICD's) that are capable of deliveringhigher energy electrical stimuli to the heart. ICD's are often used totreat patients with tachyarrhythmias, that is, hearts that beat tooquickly. Tachyarrhythmias can cause diminished blood circulation becausethe cardiac cycle of systole (contraction) and diastole (filling) can beshortened to such an extent that insufficient blood fills the ventriclesduring diastole. Besides the potential for such hemodynamicembarrassment, tachyarrhythmias can also degrade into even more seriousarrhythmias such as fibrillation where electrical activity spreadsthrough the myocardium in a disorganized fashion so that effectivecontraction does not occur. For example, in a particular type oftachyarrhythmia, referred to as ventricular fibrillation, the heartpumps little or no blood to the body so that death occurs withinminutes. A defibrillator delivers a high energy electrical stimulus orshock to the heart to depolarize all of the myocardium and render itrefractory in order to terminate arrhythmia, allowing the heart toreestablish a normal rhythm for the efficient pumping of blood. Inaddition to ICD's and pacemakers, cardiac rhythm management systems alsoinclude pacemaker/ICD's that combine the functions of pacemakers andICD's, drug delivery devices, and any other implantable or externalsystems or devices for diagnosing, monitoring, or treating cardiacarrhythmias.

Cardiac rhythm management systems incorporated into ICD's allowtachyarrhythmias to be automatically detected and treated in a matter ofseconds. Defibrillators are usually effective at treatingtachyarrhythmias and preventing death, but such devices are not 100%effective at treating all tachyarrhythmias in all patients. As a result,some patients may still die even if the defibrillator deliversappropriate therapy. Also, some patients have frequent tachyarrhythmias,triggering frequent therapeutic shocks. This reduces the usable life ofthe implanted battery-powered device and increases the risk oftherapy-induced complications. Furthermore, even if the devicesuccessfully treats the tachyarrhythmia, the patient may loseconsciousness during the arrhythmia which can result in related seriousor even fatal injuries (e.g., falling, drowning while bathing, caraccident while driving, etc.). Thus, there is a need for a cardiacrhythm management system that predicts when an arrhythmia will occur andinvokes a therapy to prevent or reduce the consequences of thearrhythmia.

SUMMARY

The present invention relates to a system and method for predictingcardiac arrhythmias. In a particular embodiment, the system and methodare implemented in an implantable cardiac device having one or moresensing channels for detecting conditioning events (e.g., marker/triggerevents as defined below) and the capability of delivering some type ofpreventive arrhythmia therapy when conditions warrant it.

In accordance with the invention, an arrhythmia is predicted by: 1)detecting a conditioning event statistically associated with theoccurrence of an arrhythmia in a patient's heart; 2) computing aconditional arrhythmia probability for the conditioning event from pastobservations of instances in which the conditioning event occurs aloneor together with an arrhythmia within a specified time period; 3)computing an estimated arrhythmia probability based upon the detectedoccurrence of the conditioning event; and 4) predicting the occurrenceof an arrhythmia within a specified prediction time period if theestimated arrhythmia probability exceeds a specified threshold value.

Conditioning events may be broadly classified into markers and triggers.A marker event corresponds to detected a physiological state that isstatistically associated with occurrence of cardiac arrhythmias, but thecausal relationship between the marker and the arrhythmia is not known.A conditioning event is regarded as a trigger, on the other hand, if theevent is thought to increase the risk of an arrhythmia occurring via adepolarization that serves as a source for the arrhythmia. Conditioningevents may be detected on a beat-to-beat basis or over a longer timeframe. Examples of conditioning events include a detected specificmorphology of a waveform representing the electrical activity of theheart, a specific pattern of activation times of different areas of theheart as sensed by a plurality of electrodes, a specific sequencepattern of heartbeats with respect to time, a value of a measuredphysiological variable such as heart rate or blood pressure, or astatistic based upon a history of occurrences of conditioning events.

In one embodiment, the conditional arrhythmia probability is calculatedas a ratio of the number of observed instances in which the conditioningevent is followed by an arrhythmia within a specified basic time period,to the total number of observed instances of the conditioning event. Inthat case, the estimated arrhythmia probability for an arrhythmia tooccur within the specified basic time period after detection of theconditioning event is simply the calculated conditional arrhythmiaprobability.

In another embodiment, the conditional arrhythmia probability CP iscalculated by the expression:

CP=1−e ^(−RT)

which assumes a Poisson probability distribution, where T is a measureof the specified prediction time period, and R is an estimate of therate at which arrhythmias occur while the conditioning event is present.The rate R is a ratio of: 1) the number of instances in which theconditioning event is followed by an arrhythmia within a specified basictime period, to 2) the length of the basic time period multiplied by thetotal number of basic time periods in which the conditioning event isobserved. The estimated arrhythmia probability for an arrhythmia tooccur within the time T after detection of the conditioning event isagain the conditional arrhythmia probability. Calculating theconditional arrhythmia probability in this manner allows the predictiontime period T to differ from the length of the basic time period used toderive the conditional arrhythmia probability.

In another embodiment, rather than basing the estimated arrhythmiaprobability upon the detection of a conditioning event, a rate at whichthe conditioning event occurs is detected over some period of time. Theestimated arrhythmia probability is then calculated as the product of anestimated probability that a conditioning event will occur times theprobability of an arrhythmia occurring within specified time periodgiven the occurrence of the conditioning event (i.e. the conditionalarrhythmia probability). Thus:

estimated arrhythmia probability=(1−e ^(−RT))(1−e ^(−CT))

where T is a measure of the specified prediction time period, R is anestimate of the rate at which arrhythmias occur while the conditioningevent is present, and C is an estimate of the rate at which theconditioning event occurs.

Another way of deriving a conditional arrhythmia probability, especiallyfor trigger-types of conditioning events (although it can be used withany type of conditioning event), is to designate a particular detectedtrigger event as being responsible for causing a detected arrhythmia.Such culpability may be assigned based, e.g., upon the proximity in timebetween the trigger event and the onset of the arrhythmia, the magnitudeof the detected trigger, or the frequency of occurrence of the triggerevent within a specific time period prior to the onset of thearrhythmia. A conditional arrhythmia probability CP for that triggerevent can then be calculated as a ratio of the number of instances inwhich the trigger event was deemed culpable for causing an arrhythmia,to the total number of occurrences of the trigger event. Also, as above,rather than basing the estimated arrhythmia probability upon thedetection of the trigger event, a rate at which the trigger event occurscan be detected over some period of time. The estimated arrhythmiaprobability is then calculated as the product of an estimatedprobability that a trigger event will occur times the probability CP ofan arrhythmia occurring within a specified time period T given theoccurrence of the trigger event. Thus:

estimated arrhythmia probability=CP×(1−e ^(−CT))

In a preferred embodiment, the prediction of arrhythmias is based upon aplurality of the same or different detected conditioning events. Acomposite estimated arrhythmia probability is then computed as acombination of the estimated arrhythmia probabilities derived for eachseparately detected conditioning event. The separately detectedconditioning events may be separate occurrences of the same or differentconditioning events. As before, the composite arrhythmia probability iscompared with a threshold value in order to predict the occurrence of anarrhythmia. In one embodiment, the composite arrhythmia probability iscalculated by adding the individual estimated arrhythmia probabilitiesderived for each detected conditioning event, which thus assumes eachindividual arrhythmia probability to correspond to an independent event.In other embodiments, specific combinations of detected conditioningevents are mapped in a non-linear fashion to estimated arrhythmiaprobabilities that can be added or otherwise combined with otherestimated arrhythmia probabilities to give a composite value. In stillother embodiments, the estimated arrhythmia probability is computed froma combination of conditional arrhythmia probabilities derived usingdifferent basic time periods but for the same prediction time period.

The past observations of the occurrences of conditioning events andarrhythmias from which the conditional arrhythmia probabilities arederived can be taken from either population data or from data collectedin real-time from a particular patient. In a preferred embodiment, theconditional arrhythmia probabilities are based initially upon pastobservations of the occurrences of events and arrhythmias taken frompopulation data, and each probability is subsequently updated from aprevious value to a present value with observations taken in real-timefrom a particular patient. In one embodiment, a conditional arrhythmiaprobability is updated only if the present value differs by apredetermined amount from the previous value. In another embodiment, theamount by which the present value differs from the previous value istested for statistical significance before a conditional arrhythmiaprobability is updated. In another embodiment, the previous value of theconditional arrhythmia probability is incremented or decremented by aspecific amount after a prediction time period in accordance withwhether the arrhythmia occurred or not, respectively.

In still another embodiment, the statistical association between theconditioning event and the occurrence of an arrhythmia is periodicallyreevaluated using the most recent patient-specific data. If thestatistical association (e.g., as a calculated from a chi-square test)is found to be below a specified value, the use of that conditionalarrhythmia probability in deriving a composite estimated arrhythmiaprobability is discontinued.

As aforesaid, one embodiment of the invention involves deliveringpreventive arrhythmia therapy if the estimated probability of anarrhythmia occurring within a specified time period (i.e., the compositeestimated arrhythmia probability) exceeds a threshold value so that anarrhythmia can be predicted with some degree of certainty. Examples ofsuch therapies capable of being delivered by an implantable deviceinclude the delivery of pharmacologic agents, pacing the heart in aparticular mode, delivery of cardioversion/defibrillation shocks to theheart, or neural stimulation (e.g., stimulation of either thesympathetic or parasympathetic branches of the autonomic nervoussystem). Another type of therapy capable of being delivered by animplantable device in accordance with the invention (interpreting theterm “therapy” in a broad sense) is issuance of a warning signal that anarrhythmia has been predicted, which warning signal may take the form ofa audible signal, a radio-transmitted signal, or any other type ofsignal that would alert the patient or physician to the possibility ofan impending arrhythmia. Such a warning signal would allow the patientto take precautionary measures and/or allow a treating physician to takeother therapeutic steps if deemed appropriate. In accordance with theinvention, the selection of a particular therapy to be delivered or notis based upon ascertaining a physiologic state of the patient anddeciding whether or not a particular available modality of therapy isappropriate for delivery to the patient in that physiologic state.

The modality selection process in one embodiment takes the form of amatrix mapping of a state vector representing a specific physiologicstate (as determined by, e.g., the particular conditioning events usedto make the arrhythmia prediction, the prediction time period for theestimated arrhythmia probability, the presence or not of specificconditioning events within a specified prior time period, or themagnitude and/or presence other detected and/or calculated variables) toa specific therapy or therapies considered most appropriate forpreventing an arrhythmia in that instance (i.e., a point in “therapyspace”). The matrix mapping is performed using a therapy matrixcontaining information relating to whether or not (and/or to whatextent) a particular therapy modality is expected to be effective for agiven physiologic state. The elements of the therapy matrix may thusconstitute variables representing the appropriateness of a particulartherapy modality given the presence of a specific element in thephysiological state vector.

In one particular embodiment, a prediction scheduler makes separatepredictions for each time period in which an arrhythmia may or may notbe predicted to occur (i.e., the prediction time period) and then makesa therapy decision using a therapy matrix specific for that predictiontime period. In this manner, a therapy decision for a given therapymodality may be made with respect to the time period appropriate forthat modality. For example, some therapy modalities can be expected tobe effective in a short time period and are thus suitable for preventingan arrhythmia with a short prediction time period, while others wouldnot be expected to be effective until after a longer time interval andtherefore would only be suitable if the prediction time period werecommensurate. In another embodiment, therapy decisions and predictionsare made at time intervals without regard to the arrhythmia predictionperiods, and the therapy matrix takes into account the prediction timeperiod for the estimated arrhythmia probability. For example, theprediction time period may be incorporated into the physiologic statevector, and the therapy matrix then contains information related to theappropriateness of each available therapy modality for a givenprediction time period.

Rather than estimating a probability for the occurrence of anyarrhythmia, separate estimated arrhythmia probabilities can be computedfor different types of arrhythmias in accordance with the invention. Bycomputing such separate probabilities, a more informed decision as towhat mode of therapeutic intervention to employ may be made. The mostbeneficial set of separate estimated arrhythmia probabilities for anyparticular patient be expected to vary. Accordingly, selection of thearrhythmia types for which separate estimated arrhythmia probabilitiesare computed can be done dynamically by tabulating the number ofdetected occurrences of each specific type of arrhythmia and computingseparate estimated arrhythmia probabilities for those arrhythmia typesoccurring most frequently in the patient. Separate estimated arrhythmiaprobabilities can also be computed for different types of triggerevents. Such trigger events can be expected to be patient-specific, andthe particular mix of trigger events for which separate arrhythmiaprobabilities are calculated can be selected dynamically as describedabove.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, like numerals describe substantially similar componentsthroughout the several views. Like numerals having different lettersuffixes represent different instances of substantially similarcomponents.

FIG. 1 is a schematic drawing illustrating generally one embodiment ofportions of a cardiac rhythm management system and an environment inwhich it is used.

FIG. 2 is a schematic drawing illustrating generally one embodiment of acardiac rhythm management device coupled by leads to a heart.

FIG. 3 is a schematic diagram illustrating generally one embodiment ofportions of a cardiac rhythm management device, which is coupled to aheart and/or other portions of the patient's body.

FIG. 4A is a block diagram illustrating generally one conceptualembodiment of portions of a trigger/marker module.

FIG. 4B is a schematic diagram illustrating generally one embodiment inwhich three electrode signals (e.g., RV, RM, LV) are used.

FIG. 5 is a timing diagram illustrating generally one embodiment ofupdating a heart rate variability marker.

FIG. 6 is a block diagram illustrating generally one conceptualembodiment of portions of an arrhythmia prediction module.

FIG. 7 is a block diagram illustrating generally one conceptualembodiment of portions of a preventive therapy control module.

FIG. 8 is a diagram illustrating generally one embodiment of atranslation matrix.

DETAILED DESCRIPTION

This invention relates to a cardiac rhythm management system thatpredicts when an arrhythmia will occur based upon the detection ofconditioning events found to be statistically associated with theoccurrences of arrhythmias. A further embodiment uses the prediction toinvoke a therapy to prevent or reduce the consequences of thearrhythmia. The present system may incorporate several featuresincluding: (1) distinguishing a degree of risk for occurrence of afuture arrhythmia, (2) making predictions that apply to a well definedfuture time interval, (3) basing predictions on direct observations ofarrhythmic triggers/markers (i.e., conditioning events) usingconditional probabilities that these arrhythmias will occur given thepast and present occurrences of the arrhythmias and thetriggers/markers, (4) assessing a confidence in the accuracy of theprediction, (5) adapting its prediction and prevention capabilities tothe individual patient, (6) assessing the expected effect of one ormultiple preventive therapies in order to determine if the likelihoodfor future arrhythmias is increased or decreased by the proposedpreventive therapies, (7) selecting a therapy based on this assessmentof its expected effect, and (8) determining whether the selectedpreventive therapy combination should be invoked based on the magnitude,timing and confidence in the arrhythmia prediction and of the expectedeffect of the selected therapy. In one embodiment, the system alsoincludes an external programmer device that can control, load andretrieve information from the implanted cardiac rhythm managementdevice, and which can process and display information obtained from theimplanted device. The present system provides techniques for predictingand preventing future cardiac arrhythmias, and such techniques can beused in combination with pacing, defibrillation, and other therapytechniques for treating presently existing cardiac arrhythmias.

In the following detailed description, reference is made to theaccompanying drawings which form a part hereof, and in which is shown byway of illustration specific embodiments in which the invention may bepracticed. These embodiments are described in sufficient detail toenable those skilled in the art to practice the invention, and it is tobe understood that the embodiments may be combined, or that otherembodiments may be utilized and that structural, logical and electricalchanges may be made without departing from the spirit and scope of thepresent invention. The following detailed description is, therefore, notto be taken in a limiting sense, and the scope of the present inventionis defined by the appended claims and their equivalents.

The present methods and apparatus will be described in applicationsinvolving implantable medical devices including, but not limited to,implantable cardiac rhythm management systems such as pacemakers,cardioverter/defibrillators, pacemaker/defibrillators, and biventricularor other multi-site coordination devices. However, it is understood thatthe present methods and apparatus may also be employed in unimplanteddevices of the same sort, as well as in external monitors, programmersand recorders.

Herein, the term “prediction” is used to mean a probability statementregarding whether or not an arrhythmia will occur at a future time. Theterm “trigger” refers to one or more cardiac events, such asdepolarizations or other intrinsic heart activity signals, that maytrigger an arrhythmia. Examples of such triggers include, by way ofexample, but not by way of limitation: sinus beats, premature sinusbeats, beats following long sinus pauses; long-short beat sequences, Ron T-wave beats, ectopic ventricular beats, and premature ventricularbeats. The term “marker” refers to one or more quantities associatedwith at least one abnormal physiologic state (e.g., the occurrence of anarrhythmia) in which the quantity is obtainable from one or moreelectrophysiologic signals, or from one or more signals from one or moreother sensors. Examples of such markers include, by way of example, butnot by way of limitation: ST elevations, heart rate, increased ordecreased heart rate, abnormal heart rate variability, late-potentials,or abnormal autonomic activity.

Overview

FIG. 1 is a schematic drawing illustrating generally, by way of example,but not by way of limitation, one embodiment of portions of a cardiacrhythm management system 100 and an environment in which it is used. InFIG. 1, system 100 includes an implantable cardiac rhythm managementdevice 105, also referred to as an electronics unit, which is coupled byan intravascular endocardial lead 110, or other lead, to a heart 115 ofpatient 120. System 100 also includes an external programmer 125providing wireless communication with device 105 using a telemetrydevice 130. In one embodiment, external programmer 125 includes a visualor other display for providing information to a user regarding operationof implanted device 105. Catheter lead 110 includes a proximal end 135,which is coupled to device 105, and a distal end 140, which is coupledto one or more portions of heart 115.

FIG. 2 is a schematic drawing illustrating generally, by way of example,but not by way of limitation, one embodiment of device 105 coupled byleads 110A-C to heart 115, which includes a right atrium 200A, a leftatrium 200B, a right ventricle 205A, a left ventricle 205B, and acoronary sinus 220 extending from right atrium 200A. In this embodiment,atrial lead 110A includes electrodes (electrical contacts) disposed in,around, or near an atrium 200 of heart 115, such as ring electrode 225and tip electrode 230, for sensing signals and/or delivering pacingtherapy to the atrium 200. Lead 110A optionally also includes additionalelectrodes, such as for delivering atrial and/or ventricularcardioversion/defibrillation and/or pacing therapy to heart 115.

In FIG. 2, a right ventricular lead 110B includes one or moreelectrodes, such as tip electrode 235 and ring electrode 240, forsensing signals and/or delivering pacing therapy. Lead 110B optionallyalso includes additional electrodes, such as coil electrodes 245A-B fordelivering right atrial and/or right ventricularcardioversion/defibrillation and/or pacing therapy to heart 115. In oneembodiment, system 100 also includes a left ventricular lead 110C, whichprovides one or more electrodes such as tip electrode 246 and ringelectrode 247, for sensing signals and/or delivering pacing therapy.Lead 110C optionally also includes one or more additional electrodes,such as coil electrodes 248A-B for delivering left atrial and/or leftventricular cardioversion/defibrillation and/or pacing therapy to heart115.

In FIG. 2, device 105 includes components that are enclosed in ahermetically-sealed enclosure, such as can 250. Additional electrodesmay be located on the can 250, or on an insulating header 255, or onother portions of device 105, for providing unipolar pacing and/ordefibrillation energy in conjunction with the electrodes disposed on oraround heart 115. Other forms of electrodes include meshes and patcheswhich may be applied to portions of heart 115 or which may be implantedin other areas of the body to help direct electrical currents producedby device 105. For example, an electrode on header 255 may be used tostimulate local muscle to provide an alert/warning to the patient. Inanother example, an additional lead is used to provide electrodesassociated with nerves or nerve ganglia, such as the vagus nerve, leftor right stellate ganglion, carotid sinus nerve, or the fat pad over theatrioventricular node. The present method and apparatus will work in avariety of configurations and with a variety of electrical contacts orelectrodes.

FIG. 3 is a schematic diagram illustrating generally, by way of example,but not by way of limitation, one embodiment of portions of device 105,which is coupled to heart 115 and/or other portions of the patient'sbody. FIG. 3 illustrates one conceptualization of various modules, whichare implemented either in hardware or as one or more sequences of stepscarried out on a microprocessor or other controller. Such modules areillustrated separately for conceptual clarity; it is understood that thevarious modules of FIG. 3 need not be separately embodied, but may becombined and/or otherwise implemented, such as in software/firmware. InFIG. 3, device 105 includes, among other things, a power source 300,such as a battery. A communication module 305 includes a telemetry orother circuit by which implantable device 105 communicates with externalprogrammer 125. A sensing module 310 senses intrinsic heart activitysignals from one or more electrodes associated with heart 115. In oneembodiment, sensing module 310 also senses other electrophysiologicalsignals. A therapy module 315 provides therapy for treatment of presentarrhythmias and prevention of future arrhythmias. In one embodiment,such therapy is provided at electrodes associated with heart 115 orportions of the nervous system such as, for example but withoutlimitation, to sense other electrophysiological signals such as activityfrom sympathetic or parasympathetic members of the autonomic nervoussystem, or to sense blood temperature or blood flow. In variousembodiments such therapy includes, among other things, pacing pulses,anti-tachyarrhythmia pacing (ATP), defibrillation shocks, etc.

In FIG. 3, sensing module 320 includes, among other things, one or moresensors, such as an accelerometer, acoustic sensor, respiration and/orstroke volume sensor (e.g., using transthoracic impedance), time and/ordate of detected arrhythmia(s), cardiac displacement or blood vesseldimensions (e.g., using ultrasonic transit time measurements), or bloodelectrolyte levels (e.g., using ion-selective membranes). Sensing module320 also includes interface circuits that receive control signals andpreprocess the sensor signal(s). Device 105 also includes a controlcircuit, such as a microprocessor or other controller 325, whichcommunicates with various peripheral circuits via one or more nodes,such as bus 330. Controller 325 includes various functional blocks, oneconceptualization of which is illustrated in FIG. 3.

In one embodiment, controller 325 includes a bradyarrhythmia controlmodule 335 that detects bradyarrhythmias based at least in part on oneor more signals obtained from sensing module 310. Bradyarrhythmiacontrol module 335 also provides one or more physiologically appropriateanti-bradyarrhythmia therapies to treat presently existingbradyarrhythmias. A tachyarrhythmia control module 340 detectstachyarrhythmias based at least in part on one or more signals obtainedfrom sensing module 310. Tachyarrhythmia control module 340 alsoprovides one or more physiologically appropriate anti-tachyarrhythmiatherapies to treat presently existing tachyarrhythmias.

For predicting and preventing arrhythmias, controller 325 also includestrigger/marker module 345, arrhythmia prediction module 350, andpreventive therapy control module 355. Trigger/marker module 345 detectsone or more triggers and/or markers based at least in part on signalsreceived from sensing module 310 and/or sensing module 320. Arrhythmiaprediction module 450 predicts the likelihood of future arrhythmiasusing probability calculations based on trigger/marker informationreceived from trigger/marker module 345. Preventive therapy controlmodule 355 selects the most appropriate therapy (or combination oftherapies) for preventing the future arrhythmia from a set of availablepreventive therapies. Preventive therapy control module 355 alsotriggers the delivery of such therapy after determining if theprobability of arrhythmia, computed by arrhythmia prediction module 350,and the expected outcome of the selected therapy warrants administrationof the therapy by therapy module 315. Thus, device 105 predicts andprevents future arrhythmias, as described more particularly below. Inone embodiment, the prediction and prevention of future arrhythmias isdeactivated if one or more present arrhythmias (e.g., ventriculartachyarrhythmia (VT) or ventricular fibrillation (VF)) are present andare being treated by techniques, known to one skilled in the art, usingtachyarrhythmia control module 340 and/or bradyarrhythmia control module335.

Example of Detecting Arrhythmogenic Trigger(s)/Marker(s)

FIG. 4A is a block diagram illustrating generally, by way of example,but not by way of limitation, one conceptual embodiment of portions oftrigger/marker module 345. In this embodiment, trigger/marker module 345includes beat classification module 400, detection processing module405, and a trigger/marker data bank such as trigger/marker list 410.Trigger/marker module 345 recurrently examines signals from sensingmodule 310 and/or sensing module 320 and detects the presence, timing,and (if appropriate) magnitude of triggers/markers. These detections,timings, and magnitudes are output to detection processing module 405either for each heartbeat, or corresponding to a time periodencompassing multiple heartbeats.

Beat classification module 400 examines signals from variously locatedelectrodes. On a beat-to-beat basis, beat classification module 400distinguishes between depolarization events that spread over the heartnormally from those that spread out over the heart abnormally. Normalbeats refer to those beats that are most prevalent and physiologicallysound in the patient, even though the patient's most prevalent beat typemight not be considered normal when compared to the propagation of adepolarization in a healthy heart. In one embodiment, sensing module 310provides and beat classification module analyzes signals from: a rightatrial sensing electrode (RA), a right ventricular sensing electrode(RV), a left ventricular (LV) sensing electrode, a right sidedmorphology (RM) signal between defibrillation electrodes in the rightventricular and superior vena cava and/or the metallic shell of thedevice, a left sided morphology (LM) signal between a left ventricularelectrode and one or more of the other electrodes. Several examplemethods of operating beat classification module 400 to classify heartbeats as normal or abnormal are described below by way of example, butnot by way of limitation.

A first method to distinguish normal and abnormal beats detectsdepolarizations on a plurality of electrode signals for each beat. Inone example of this method, an RV depolarization indicates that a beathas occurred. The time intervals between detection of the samedepolarization at the various electrodes (RA, RV, LV, RM, LM) reflectthe pattern and timing with which this depolarization propagates overthe heart. FIG. 4B is a schematic diagram illustrating generally, by wayof example, but not by way of limitation, one embodiment in which 3electrode signals (e.g., RV, RM, LV) are used. The sensed depolarizationon each electrode signal resets 3 interval timers (total of 9 timers)that measure time intervals initiated by the resetting event. The senseddepolarization on each electrode signal also latches the current valuein 3 timers, each of which is reset by a different electrode signal, asillustrated in FIG. 4B. In this way, each time a depolarization passesover the heart, the set of 9 time intervals is obtained. These timeintervals represent the pattern with which the depolarization reacheddifferent electrodes. Similarly, if 4 signals are used, then 16intervals exist, and if 5 signals are used, then 25 intervals exist,etc. If the set of intervals for a particular beat fall outside oflimits deemed normal, then that beat is classified as abnormal. In oneembodiment, the abnormal limits vary with the heart rate. The abnormallimits may be programmed into the device as either population-based orpatient-specific values.

A second method to distinguish normal beats from abnormal beats comparesthe morphology of one or more available signals (e.g., RV and/or RMand/or LM signals) with corresponding template morphologies for a normalbeat. In one embodiment, an RV depolarization detection provides atriggering event. For each RV depolarization, a correlation coefficientbetween the selected morphology signal and a corresponding templatemorphology is computed. If the coefficient is smaller than apredetermined threshold value, then the current beat is classified asabnormal. In one embodiment, different template morphologies are usedfor different ranges of heart rates. Also, the threshold values may beprogrammed into the device using either population-based orpatient-specific values.

A third method to distinguish normal and abnormal beats extractsfeatures from one or more available signals and compares the extractedfeatures with similar features for normal beats. Possible featuresinclude, by way of example, but not by way of limitation, QRS duration,R-wave amplitude, QT interval, and T-wave amplitude. For a particularbeat, if the detected value of one or more features falls outside limitsdeemed normal, then the beat is classified as abnormal. In oneembodiment, the normal limits vary with heart rate. Also, the normallimits may be programmed into the device either as population-based orpatient-specific values.

In addition to these methods of classifying beats, other methods ofidentifying or classifying beats will also be suitable. Also, beatclassification module 400 can also be used with lead configurationshaving a different number of electrodes or electrodes located indifferent locations. After beat classification module 400 classifies adetected heart beat, detection processing module 405 performs theprocessing required for trigger/marker detection.

In one embodiment, detection processing module 405 uses the beat-to-beatintervals and morphological data extracted from the signals by beatclassification module 400 and also extracts any additional morphologicalmeasures required for trigger/marker detection. Detection processingmodule 405 is also capable of examining activity, respiration, andstroke volume signals received from one or more acceleration, acousticand/or impedance sensors and provided by sensing module 320.

Device 105 includes a trigger/marker list 410, which lists thetriggers/markers to be detected. List 410 includes members, for example,the types and natures of the particular trigger/marker sought to bedetected. In one embodiment, each member of the list includes acorresponding predetermined comparison value. In one example, list 410includes a member “long QRS duration” trigger/marker. For that member,list 410 also includes a comparison value indicating which QRS durationsare long. In another example, list 410 includes a particular“short-long-short sequence” of intervals between beats classified asnormal. For that member, list 410 also includes corresponding comparisonvalues establishing the criteria for long and short intervals in thesequence. Alternatively, list 410 includes members that include one ormore beats classified as abnormal.

In one embodiment, the type and nature of the triggers/markers in list410 are obtained from research in the relevant clinical population.Several different types of triggers/markers exist. For example, a firstbasic type of triggers/markers, is referred to as “sequence type”triggers/markers. Sequence type triggers/markers are based on sequencesof one or more beats. Examples of sequence type triggers/markersinclude, by way of example, but not by way of limitation, prematurenormal beats, premature abnormal beats, delayed normal beats, delayedabnormal beats, long-short intervals between beats, short-long intervalsbetween beats, and multiple (couplets, triplets, etc) abnormal beats.Detection processing module 405 detects and counts the number of timeseach of the specified sequence type triggers/markers are present duringa particular observation period.

An example of a second basic type of triggers/markers is referred to as“value type” triggers/markers. Value type triggers/markers are based onmetrics from the available signals, which are compared to predeterminedcomparison values to determine whether the trigger/marker is presentduring a particular observation period. Examples of value typetriggers/markers based on morphology intrinsic heart activity signalsinclude, by way of example, but not by way of limitation, QRS duration,ST magnitude, QT interval, and R-wave amplitude. In one embodiment,detection of morphology-based value type triggers/markers is based onlyon consideration of one or more beats classified as normal. In oneexample, a trigger/marker is based on the most recent beat. In anotherexample, the trigger/marker is based on an average of morphology valuesover some number of previous normal beats. In a further example, thetrigger/marker is based on one or more abnormal beats. Other examples ofvalue type triggers/markers are not based on the heart's electricalactivity. Such value type triggers/markers include, by way of example,but not by way of limitation, present respiratory rate, a position inthe respiratory cycle at which the beat is detected, tidal volume of arespiratory inhalation or exhalation, cardiac stroke volume, presentactivity level of the patient, or the current position within thediurnal cycle.

An example of a third basic type of triggers/markers is referred to as“history type” triggers/markers. In one embodiment, the presence orabsence of history type triggers/markers is determined based on one ormore possible computational analyses of historical data obtained frommultiple beats or from multiple observation periods. Examples of historytype triggers/markers include, by way of example, but not by way oflimitation, a percentage of abnormal beats detected during anobservation period, a percentage of premature or ectopic beats detectedduring an observation period, heart rate variability during anobservation period (e.g., 5 minutes), and the presence of alternans(i.e., cyclic variations over a plurality of cardiac cycles, e.g.,T-wave alternans or QRS alternans) during an observation period. Otherexamples of history type triggers/markers include, by way of example,but not by way of limitation, short-term and long-term averages of thebeat-to-beat data, trends in such averages (e.g., increasing ordecreasing ST elevation, increasing density of abnormal or prematurebeats, etc.), and trends or periodicities in sensor values obtained fromsensing module 320.

In one embodiment, detection processing module 405 outputs a set ofdetection values, denoted D_(I), D₂, D₃, . . . , D_(N), corresponding tothe N members in list 410 and where each detection value, D_(i),corresponds to a single member of list 410. In one embodiment, adetection value D_(i) is set to 0 if the corresponding trigger/markerwas not detected during the most recent observation period, and is setto 1 if the trigger/marker was detected during the most recentobservation period.

Timing of Arrhythmia Predictions and Trigger/Marker Detection

Not all trigger/marker detections are updated on a beat-to-beat basisbecause some triggers/markers (e.g., history type triggers/markers)generally require information obtained from multiple beats. In oneembodiment, the detection of triggers/markers is performedasynchronously from arrhythmia predictions. Several techniques can beused for this purpose. In one technique, each trigger/marker detectionvalue corresponds to a predetermined observation period during whichtime its value (e.g., present, or not present) is determined. For manytriggers/markers, the appropriate observation period is equal to apredetermined basic time period (BTP). For instance, if the basic timeperiod is 2 minutes, then device 105 tests for the occurrence of suchtriggers/markers in the last 2 minutes. However, detection observationperiods are not necessarily the same for each trigger/marker. In oneembodiment, for example, a heart rate variability trigger/marker isbased on an observation period of approximately 5 minutes.

FIG. 5 is a timing diagram illustrating generally, by way of example,but not by way of limitation, a heart rate variability trigger/marker,D_(I), which is updated using a 5 minute observation period. In thisexample, D_(I)=1 during time periods 500 when heart rate variability isbelow its detection threshold and D_(I)=0 during other time periods 505when heart rate variability is above its detection threshold. FIG. 5also depicts a “long-short sequence” trigger, D₂, which is updated at 2minute intervals. In this example, D₂=1 during time periods 510 when thelong-short sequence has been detected in the preceding 2 minuteobservation period, and D₂=0 at other times. In this way, particulartrigger/marker detection values are updated independently of othertrigger/marker detection values in the set of trigger/marker detectionvalues, D, as sufficient data for making the particular trigger/markerdetection becomes available.

In one embodiment, predictions of future arrhythmias are made atapproximately regular time intervals 415, such as at the BTP. In oneembodiment, the BTP is based on a fixed number of beats (e.g., 60beats). In another embodiment, the BTP is based on the number of beatsclosest to a fixed duration (e.g., the number of beats just exceeding 1minute, such that the BTP remains approximately constant. In a furtherembodiment, the time duration of the BTP is adjusted over a period oftime during which device 105 is being used, such that predictions offuture arrhythmias are made more or less frequently at times when thepredicted probability for arrhythmias is higher or lower, respectively.

Other Techniques for Timing Predictions of Arrhythmias

According to one aspect of the present arrhythmia prediction andprevention techniques, the prediction provides a good match between thetime period covered by the prediction and the requisite times for one ormore potential preventive therapies. For example, a prediction of a99.9% chance that the patient will have an arrhythmia within the next100 years is highly accurate but worthless because the prediction doesnot relate to when the prevention therapy should be delivered or whichprevention therapy should be provided. Similarly, a prediction of a 50%chance that the patient will have an arrhythmia within the next 2 yearsis of limited value because it relates only to long-term preventiontherapies such as selecting patients for device implant or for long-termdrug or pacing therapy. By contrast, a prediction of a 50% chance thatthe patient will have an arrhythmia within the next 30 seconds providesgreat value in an arrhythmia prediction and prevention scheme in animplanted device provided that the decision to invoke a preventivetherapy and the requisite time of action for that therapy can be carriedout quickly enough to significantly reduce the chance of the futurearrhythmia. In this example, the value of the prediction would be quitelimited if the available preventive therapy required, for example, 20minutes to take effect.

According to another aspect of the present arrhythmia prediction andprevention techniques, the prediction frequency adequately relates tothe time period for which the prediction is valid. For example, it makeslittle sense for a prediction covering 1 month to be made once every 5seconds or for a prediction covering 5 seconds to be made once everymonth. Thus, the present arrhythmia prediction and prevention techniquesbalance between the time period covered by a prediction, the frequencywith which it is made, and the time required for the preventive therapyto take effect.

In one embodiment, device 105 does not necessarily make the same set ofpredictions each time predictions are made. One method to do so for thedevice to make one set predictions for each BTP that cover times on theorder of the BTP. Then at BTP multiples that correspond to longerintervals (e.g. 20 minutes, 1 hour, 1 day, 1 week, 1 month), acorresponding sets of additional predictions are made which cover thecorrespondingly longer time intervals. The scheduling of thepredictions, their corresponding time intervals are selected, accordingto the above argument, according to the times of action for theprevention therapies available to the device 105.

Arrhythmia Prediction Example

FIG. 6 is a block diagram illustrating generally, by way of example, butnot by way of limitation, one conceptual embodiment of portions ofarrhythmia prediction module 350. In one embodiment, arrhythmiaprediction module 350 includes an arrhythmia probability calculationmodule 600, a conditional probability data bank such as a list ofconditional probabilities 605, trigger/marker use data bank such astrigger/marker use list 610. In a further embodiment, arrhythmiaprediction module 350 also includes an adaptive processing module 615.In one embodiment, conditional probability list 605 and trigger/markeruse list 610 each have members corresponding to the members oftrigger/marker list 410, as explained below.

In one embodiment, arrhythmia prediction module 350 includes an inputthat receives the detection values (D_(I), . . . D_(N)) provided bytrigger/marker module 345. In a further embodiment, arrhythmiaprediction module 350 also includes input(s) that receive one or more“arrhythmia detected” signals provided by conventional arrhythmiadetection modules of device 105 (e.g., signals from bradyarrhythmiacontrol module 335 and tachyarrhythmia control module 340). These“arrhythmia detected” signals indicate, among other things, the presenceor absence of one or more present bradyarrhythmias or tachyarrhythmias.Arrhythmia prediction module 350 outputs an arrhythmia prediction topreventive therapy control module 355, which, in turn, bases delivery ofpreventive therapy on the arrhythmia prediction.

One aspect of the present arrhythmia prediction techniques treats theoccurrence of an arrhythmia during a particular time period as a randomevent. In this embodiment, an arrhythmia prediction includes aprobability calculation estimating the probability that an arrhythmiawill occur within a specified period after the prediction. An example ofsuch a prediction is a 50% probability that an arrhythmia will occurwithin the next 2 minutes. This method of prediction includes both adegree (e.g., 50%) and a well defined time period during which theprediction is applicable (e.g., 2 minutes), as opposed to merelyindicating that the patient is currently at risk.

In one embodiment, the arrhythmia prediction output from arrhythmiaprediction module 350 includes a set of one or more arrhythmiaprobability assertions or statements. Each probability statementincludes both a magnitude of the probability and a specified future timeperiod associated therewith. In one embodiment, each probabilitystatement also identifies which trigger, marker, or combination oftrigger(s) and/or marker(s) contributed to its magnitude. In a furtherembodiment, the time period covered by each probability statement (i.e.,the time period over which each probability statement is valid) isdetermined by, among other things, the scheduled prediction frequency(e.g., predictions made at 1 minute intervals cover a 1 minute period,etc.).

One conceptual approach to prediction recognizes that the rate at whicharrhythmia events randomly occur changes relatively slowly compared tothe time period over which predictions are made (i.e., the predictionfrequency). Because future arrhythmia rates cannot be measured,predictions are based on one or more previous and/or present estimatesof the arrhythmia rate assuming that the arrhythmia rate doesn't changesignificantly during the time period over which the prediction is made.The probability that a random arrhythmia event will occur within thespecified prediction time period can be calculated based on the presentarrhythmia rate. For simplicity, the occurrence of arrhythmia events canbe understood as the result of a Poisson process with a rate, R. Theprobability, P, that one or more arrhythmias will occur within a nexttime period, T, is P=(1−e^(−RT)). The accuracy of P depends on thedegree to which the estimate for R is correct, the rate R is trulyconstant during the time period T, and whether the underlying process istruly a Poisson random process. While such a conceptual approach may behelpful to understand the context of certain of the present predictiontechniques, in one embodiment arrhythmia prediction module 305 does notactually formulate an estimate of the underlying arrhythmia rate.Instead, the arrhythmia rate is reflected in the presence or absence ofthe detected triggers/markers, which are used by arrhythmia probabilitycalculation module 600 to formulate a probability for a futurearrhythmia, as explained below.

For example, consider the Ith trigger/marker of trigger/marker list 410.In one embodiment, the detection value D_(I) is either one or zerodepending on the respective presence or absence of this trigger/markerduring the basic time period equal to T. Further, consider that the rateof arrhythmias R_(I) associated with this trigger/marker is zero whenthe trigger/marker is absent and that the rate R_(I) has a nonzero valuewhen the trigger/marker is present. The contribution of the Ithtrigger/marker to the arrhythmia probability is D_(I) (1−e^(−RT)), orsimply D_(I)×C_(I) where CP_(I) is the conditional probability for thearrhythmia given that D_(I) is present. The total probability for anarrhythmia, P, which includes the contributions from alltrigger/markers, is computed as P=D_(I)CP_(I)+D₂×CP₂+D₃×CP₃+ . . .+D_(N)×CP_(N), for the case where there is a detection value and aconditional probability for each member of the trigger/marker list 410.

In one embodiment, the conditional probabilities CP are obtainedempirically from observations made in a patient population. In anotherembodiment, the conditional probabilities CP are seeded withpopulation-based values, but are later adapted to the individual patientbased on empirical observations of that patient. Because the conditionalprobabilities CP are empirically determined, the underlying process neednot be exactly Poisson distributed, and the rate need not be completelyunchanging during the time period T. The empirical estimations for theconditional probabilities CP incorporate deviations from theseassumptions, although their predictive accuracy may be reduced. In oneembodiment, a trigger/marker list 410 and a population-based conditionalprobability list 605 is loaded into device 105 before, during, or afterdevice 105 is implanted in the patient.

In one embodiment, arrhythmia prediction module 350 also includes atrigger/marker use list 610, which provides a set of usage weights orflags, each usage weight or flag associated with a correspondingtrigger/marker of trigger/marker list 410. According to one aspect ofthe present techniques, it may be desirable to exclude one or more ofthe trigger/marker detections from the total probability computation,such as, as the result of learning patient-specific traits by adaptiveprocessing module 615. In trigger/marker use list 610, each usage weightor flag indicates how that trigger/marker detection value andconditional probability enters into the arrhythmia probabilitycomputation. In one embodiment, usage flags provide a binary indicationof whether or not that particular trigger/marker detection value andconditional probability enters into the arrhythmia probabilitycalculation. In another embodiment, usage weights provide a degree towhich the detection value and conditional probability enters into thearrhythmia probability calculation. In such an embodiment, thearrhythmia probability calculation may be normalized, based on thevalues of the usage weights, such that the arrhythmia probability rangesbetween 0 and 1.

Determining Triggers/Markers and Conditional Probabilities from aPopulation

In one embodiment, a relevant patient population is used to obtaininitial or actual estimates for conditional probabilities and/or toselect particular triggers/markers for active use in the presentprediction techniques. Patient-specific and adaptive techniques aredescribed later. One approach for determining population-basedparameters uses long-term data (e.g., from sensing module 310 and/orsensing module 320) from a representative subset of the clinicallyrelevant population. This long-term data obtained from a plurality ofpatients in the clinically relevant patient population is referred to asthe population database. Data in the population database is divided intotime periods equal to the BTP. After each such time period,trigger/marker detection processing is performed over the entirepopulation database for all triggers/markers. The presence or absence ofeach trigger/marker (i.e., the set of detection values, D) is notedafter each such time period. The presence or absence of one or morearrhythmias is also noted after each such time period. Onepopulation-based estimate for a particular conditional probabilityassociated with the Ith trigger/marker (i.e. CP_(I)) is simplyCP_(I)=[(total number of time periods with arrhythmias that werepreceded by a time period with D_(I) present)÷(total number of timeperiods with D_(I) present)]. A similar estimate is made for all membersin trigger/marker list 410. This embodiment uses time periods equal tothe same duration (i.e., the BTP) for all triggers/markers.Alternatively, the population database is divided into equal-length timeperiods that are different between individual triggers/markers. Forexample, for each trigger/marker, the population database may be dividedup into equal length time periods that correspond approximately to thetime period covered by that particular trigger/marker.

Determining Predictive Capability of Particular Triggers/Markers

Not all possible triggers/markers may have predictive power, either inthe clinically relevant patient population, or in the particular patienttoward which the disclosed techniques are applied. The presenttechniques include a method of selecting those particulartriggers/markers, in the trigger/marker list 410, that have usefulpredictive capability. One such method uses a Chi squared test based onthe population database, but other statistical test methods may also beused. For example, in the Chi squared test, the presence or absence ofarrhythmias and the available triggers/markers in each time period inthe population database yields the following values: #BTP=total numberof BTP's examined, #D⁺=total number of BTP's in which the trigger/markerwas detected, #D⁻=total number of BTP's in which the trigger/marker wasnot detected, #A⁺=total number of BTP's in which arrhythmia wasdetected, #A⁻=number of BTP's in which arrhythmia was not detected, #D⁺A⁺=number of BTP with the trigger/marker detected followed by a BTPwith arrhythmia, # D⁺A⁻=number of BTP with the trigger/marker detectedfollowed by a BTP without arrhythmia, # D⁻A⁺=number of BTP without thetrigger/marker detected followed by BTP with arrhythmia, # D⁻A⁻=numberof BTPs without the trigger/marker detected followed by BTP withoutarrhythmia.

A statistical test determines whether the observed occurrences of #D⁺A⁺,#D⁺A⁻, #D⁻A⁺, and #D⁻A⁻ are different from those that would be expectedif the trigger/marker did not have predictive power. One example suchtest computes the following sum:${Sum} = {\frac{\left\lbrack {{\# D^{+}A^{+}} - {\# A^{+} \times \# {D^{+}/\#}{BTP}}} \right\rbrack^{2}}{\# A^{+} \times \# {D^{+} \div \#}{BTP}} + \frac{\left\lbrack {{\# D^{+}A^{-}} - {\# A^{-} \times \# {D^{+}/\#}{BTP}}} \right\rbrack^{2}}{{\# A^{-} \times \# D^{+}} \equiv {\# {BTP}}} + \frac{\left\lbrack {{\# D^{-}A^{+}} - {\# A^{+} \times \# {D^{-}/\#}{BTP}}} \right\rbrack^{2}}{\# A^{+} \times \# {D^{-}/\#}{BTP}} + \frac{\left\lbrack {{{\# D^{-}A^{-}} - {\# A^{-} \times \# D^{-}}} \equiv {\# {BTP}}} \right\rbrack^{2}}{{\# A^{-} \times \# D^{-}} \equiv {\# {BTP}}}}$

This represents the Chi Square value (1 degree of freedom) that tests ifthe arrhythmia is associated with the trigger/marker. If the sum exceeds3.84, for example, we are 95% confident that the trigger/marker providespredictive capability for the arrhythmia. A more complete multi-variatestatistical analysis would provide stronger conclusions about thetrigger/marker's role when considered together with othertriggers/markers having independent predictive power. Strictly speaking,the form for the above probability computation is based on anapproximation, i.e., that the predictive capability of the individualtriggers/markers are independent from each other.

Patient Specific Adaptive Processing Example

In one embodiment, device 105 includes a patient specific adaptiveprocessing module 615, as illustrated in FIG. 6. In one example,adaptive processing module 615 modifies conditional probability list 605and trigger/marker use list 610. The present techniques recognize thatit is unlikely for some set of one or more triggers/markers to be sopowerful at predicting arrhythmias that they always worked in allpatients. The predictive capability of a particular set of one or moretriggers/markers will most likely vary between different patients.Population-based average values may be suboptimal for a particularpatient. In one embodiment, device 105 examines long-term data specificto the individual patient in which it is implanted. Thus, one aspect ofthe present techniques allow device 105 to adapt to an individualpatient to improve the accuracy and confidence in arrhythmia predictionsover a time during which device 105 is being used.

One embodiment extracts patient-specific parameters (e.g., conditionalprobabilities and/or weights) analogously to the techniques describedabove that extract population-based parameters. In this embodiment, themembers of the trigger/marker list 410 are initially established basedon clinical research in a population of interest, and are loaded intodevice 105. After implantation of device 105 in a particular patient, asongoing data is acquired from the patient, adaptive processing module615 manages trigger/marker use list 610 by determining which members oftrigger/marker list 410 provide predictive capability for arrhythmiaprediction in the particular patient.

One such technique initially sets the members of the trigger/marker uselist 610 to “do not use.” At some point after implant, adaptiveprocessing module 615 examines each BTP to detect the presence orabsence of (1) arrhythmias and (2) the triggers/markers intrigger/marker list 410 to form a patient-specific database. Device 105recurrently performs the above Chi squared test to determine (to withina predetermined level of confidence) if each member of trigger/markerlist 410 provides predictive capability in the particular patient. If amember does provide such predictive capability, the usage value for thatmember is set to “use” in trigger/marker use list 610. Such a techniqueonly allows an arrhythmia prediction based on a particulartrigger/marker after sufficient confidence exists that its predictivecapability in the particular patient is sufficiently high.

Another such technique initially sets the members of the trigger/markeruse list 610 to “use,” such as where population based data indicates thelikelihood that these members of the trigger/marker use list are likelyto have sufficient predictive capability. In this embodiment, thecorresponding usage values are set to “do not use” if thepatient-specific database demonstrates that the trigger/marker does notprovide adequate predictive capability in the particular patient despiteits good performance in the population. Such a technique stops using thetrigger/marker to make predictions after sufficient confidence existsthat its predictive capability in the particular patient is inadequate.

For patient-specific adaptive processing, the ratios (R⁺ _(P)=# D⁺A⁺÷#D⁺) and (R⁻ _(P)=# D⁻A⁺÷# D⁻) from the population database (hence the Psubscripts) are used to compute the following sum:${Sum} = {\frac{\left( \left\lbrack {{\# \quad {\quad D^{+}}\quad A^{+}}\quad - \quad {R_{P}^{+} \times \# \quad {\quad D^{+}}}} \right\rbrack \right)^{2}}{R_{P}^{+} \times \# \quad {\quad D^{+}}}\quad + \quad \frac{\left( \left\lbrack {{\# \quad {\quad D^{+}}\quad A^{-}}\quad - \quad {\left( {1\quad - \quad R_{P}^{+}} \right) \times \# \quad {\quad D^{+}}}} \right\rbrack \right)^{2}}{{\left( {1\quad - \quad R_{P}^{+}} \right) \cdot \#}\quad {\quad D^{+}}}\quad + \quad \frac{\left( \left\lbrack {{\# \quad {\quad D^{-}}\quad A^{+}}\quad - \quad {R_{P}^{-} \times \# \quad {\quad D^{-}}}} \right\rbrack \right)^{2}}{R_{P}^{-} \times \# \quad {\quad D^{-}}}\quad + \quad \frac{\left( \left\lbrack {{\# \quad {\quad D^{-}}\quad A^{-}}\quad - \quad {\left( {1\quad - \quad R_{P}^{-}} \right) \times \# \quad {\quad D^{-}}}} \right\rbrack \right)^{2}}{\left( {1\quad - \quad R_{P}^{-}} \right) \times \# \quad {\quad D^{-}}}}$

where the values of # D⁺, # D⁺A⁺, etc. are obtained from the patientspecific database. This sum, which is a Chi squared value, is comparedto another predetermined threshold value. If the sum exceeds thethreshold, then that trigger/marker does not predict the arrhythmia inthe specific patient in the same manner as it does in the population.This could occur either if the trigger/marker provides lesser predictivepower than in the population, or also if the trigger/marker providesgreater predictive power than in the population. Consequently, thecorresponding usage flag in trigger/marker use list 610 is only set to“do not use” if the patient-specific test also demonstrates that thetrigger/marker does not provide predictive power in the particularpatient. In one embodiment, these threshold values (population-basedand/or patient-specific) are programmable. In a further embodiment,these threshold values (population-based or patient-specific) areprogrammed to either the same value, or to different values.

The predictive capability of a particular trigger/marker could changeover time in a particular patient. In one embodiment, adaptiveprocessing module 615 recurrently sets and/or resets the usage values inthe trigger/marker use list 610 over time while device 105 is beingused. In this embodiment, testing for patient-specific predictivecapability of each trigger/marker is carried out recurrently. If aparticular trigger/marker is not presently in use, then the test result(i.e., the sum described above) would be required to exceed a firstthreshold value to change the usage value of the trigger/marker to“use.” If the trigger/marker is presently in use, then the test result(i.e., the sum described above) would be required to exceed a secondthreshold value to change the usage value of the trigger/marker to “donot use.” These threshold values can either be the same, oralternatively can be different to provide hysteresis that would reducethe number of spurious transitions.

In one embodiment, adaptive processing module 615 also provides andadaptively modifies conditional probabilities in conditional probabilitylist 605 based on observations in the specific patient. For a particularpatient, a trigger/marker's conditional probability may vary from thepopulation-based value. Conditional probability list 605 is initiallyseeded with population-based conditional probabilities. However, asdevice 105 acquires data from the particular patient, patient-specificconditional probabilities are used, as described below.

In one embodiment, adaptive processing module 615 implements thepatient-specific conditional probabilities by recurrently estimating theconditional probability for each trigger/marker analogously to theirpopulation-based estimation described above. However, patient-specificdata is used rather than population-based data. For each member of thetrigger/marker list 410, device 105 estimates the patient-specificconditional probability as CP_(I est)=# D⁺ _(I)A⁺÷# D_(I) ⁺, where #D_(I) ⁺ is number of BTP's in which the Ith trigger/marker was detected,and # D_(I) ⁺A⁺ is the number of BTP in which (1) trigger/marker I wasdetected, and (2) the BTP was followed by a BTP with an arrhythmia.

Such recurrent reforecasting of conditional probabilities also includesa second step of determining when the patient-specific values should beused rather than the population-based values. Counters storing # D_(I)⁺A⁺ and # D_(I) ⁺ are initially reset to zero. Whenever a BTP containingtrigger/marker I is detected, device 105 formulates a new estimate forthe conditional probability CP_(I est) by incrementing # D_(I) ⁺ and #D_(I) ⁺A⁺ if the following BTP includes an arrhythmia.

If the initial value for CP_(I) is also correct for the particularpatient, then after observing # D_(I) ⁺ BTP's with trigger/marker I, onewould expect (# D_(I) ⁺×CP_(I)) cases where an arrhythmia was found.More or fewer arrhythmias may result from chance alone. The standarddeviation for the number of expected arrhythmias is { # D_(I) ⁺×CP_(I)(1−CP_(I))}^(½). A 95% confidence interval would include about 1.96times this standard deviation above and below the expected values. Thus,if the initially entered values for CP_(I) were also valid for thisparticular patient, then there exists a 95% confidence that the numberof arrhythmias observed after # D_(I) ⁺ occurrences of trigger/marker Iis between [# D_(I) ⁺×CP_(I)−1.96 {# D_(I) ⁺×CP_(I)×(1−CP_(I))}^(½)] and[# D_(I) ⁺×CP_(I)+1.96 {# D_(I) ⁺×CP_(I)×(1−CP_(I))}^(½)]. Expressed aspercentages of # D_(I) ⁺, this yields the following confidence intervalfor the conditional probability: [CP_(I)−1.96×{CP_(I)×(1−CP_(I))/# D_(I)⁺}^(½)] and [CP_(I)+1.96×{CP_(I)×(1−CP_(I))/# D_(I) ⁺}^(½)]

If the estimate for the conditional probability CP_(I est) falls outsidethis range, there exists a 95% confidence that CP_(I est) is a betterestimate for this particular patient than CP_(I). In that case, theestimated value for the conditional probability is used for subsequentpredictions of future arrhythmias rather than the population-basedvalues. Because the patient's conditional probabilities may change overtime, the same procedure can be repeated recurrently by using the newconditional probability as the given value and forming another newestimate based on a new set of observations. In this way, theconditional probabilities are recurrently updated to more appropriatevalues as device 105 is being used.

One alternate method of updating conditional probabilities recurrentlyupdates the patient-specific conditional probability for eachtrigger/marker in such a way that the values eventually approach ortrack the true values for the specific patient. In this method, theconditional probabilities are seeded with population-based values. Then,each time a BTP containing one of the triggers/markers is observed,device 105 notes the presence or absence of an arrhythmia during thenext BTP. If an arrhythmia is present, then the conditional probabilityfor that trigger/marker is increased by a small step (e.g.,CP_(I)=CP_(I)+STEP*(1−CP_(I))). If the arrhythmia is not present, thenthe conditional probability for that trigger/marker is decreased by asmall step, as illustrated below (e.g., CP_(I)=CP_(I)−STEP*(CP_(I))).

If the patient's true conditional probability equals CP_(I), then theratio of BTP's with arrhythmias absent to those with arrhythmias presentshould equal (1−CP_(I))/CP_(I). For a conditional probability of 20%,for example, an average of 4 BTPs without arrhythmias are expected foreach BTP with an arrhythmia. In this case, the conditional probabilityis decreased 4 times by a small amount (0.2×STEP) but increased 1 timeby a large amount (0.8×STEP), yielding no net drift in the CP_(I) overtime. If the patient's actual conditional probability was significantlyhigher than CP_(I), then there would be a higher proportion of BTP'swith an arrhythmia. As a result, CP_(I) would tend to increase untilCP_(I) approached the correct value. Similarly, if the actual value wastoo low, CP_(I) would decrease until it approached the correct value. Inone embodiment, the selection of STEP is small (˜0.05) such that thesechanges are stable over time.

Examples of Alternate Methods for Arrhythmia Prediction

In one alternate embodiment, arrhythmia prediction module 350incorporates into an arrhythmia prediction the number of times atrigger/marker occurs during a BTP or other time period. An arrhythmiamay occur each time a particular trigger/marker occurs. Repeatedappearances of a trigger/marker may suggest a stronger relativepredictive value. Consequently, multiple occurrences of a trigger/markerprovide a higher arrhythmia probability. In this embodiment, each memberin the conditional probability list 605 includes a set of one or moreconditional probability values. In one example, the first value in theset reflects the conditional probability when the correspondingtrigger/marker occurred once during the basic time period, the secondset reflects the conditional probability when the correspondingtrigger/marker occurred twice during the basic time period, etc. In thisembodiment, the set of conditional probabilities may alternativelyreflect ranges of the number of trigger/marker detections (e.g., between3 and 5 detections, fewer that 8 detections, more than 3 detections,etc.). The population-based and patient-specific values for these setsof conditional probabilities are also obtained by extension of thetechniques described above. The patient-specific adaptive processing forthese sets of conditional probabilities is also obtained by extension ofthe techniques described above.

This alternate embodiment also provides a different technique oftrigger/marker detection. Instead of setting the detection value, D_(I),to either zero or one, the trigger/marker detection processing module405 sets the detection value D_(I) to the number of times the Ithtrigger/marker occurred during the BTP. The arrhythmia probabilitycalculation is computed as P=D_(I)×P_(I,D1)+D₂×CP_(2,D2)+D₃×CP_(3,D3)+ .. . +D_(N)×CP_(N,DN), where CP_(I,K) is the conditional probability whentrigger/marker I is present K times during the BTP, and N is the numberof triggers/markers.

A second alternate embodiment recognizes that if two (or more) differenttriggers/markers occur during the same BTP, the probability forarrhythmias may be different from the linear sum of the conditionalprobabilities when the trigger/marker occurred alone (i.e., without theother one or more triggers/markers). In this embodiment, detectionprocessing module 405 outputs binary detection values for the members inthe trigger/marker list 410. However, in this embodiment, arrhythmiaprediction module 350 forms a number from 0 ( none of triggers/markersin trigger/marker list 410 detected) to 2^(N)−1 (all of thetriggers/markers in trigger/marker list 410 detected) indicating whichof the triggers/markers in trigger/marker list 410 were detected duringthe BTP, where N is the number of members in trigger/marker list 410. Inthis embodiment, the members of conditional probability list 605 andtrigger/marker use list 610 do not correspond to the members oftrigger/marker list 410. Instead, the members of conditional probabilitylist 605 and trigger/marker use list 610 correspond to the variouscombinations of the 2^(N)−1 possible combinations of triggers/markersthat could be detected. The arrhythmia probability calculation iscomputed as P=CP_(K), where K is a number (from 0 to 2^(N)−1) thatreflects the current combination of detected triggers/markers (i.e.,those triggers/markers having a detection value of 1).

Automated Preventive Therapy Selection Processing Example

Preventive therapy control module 355 automatically decides whether toinvoke preventive therapy based on arrhythmia probability statementsobtained from arrhythmia prediction module 350. Preventive therapycontrol module 355 provides output signals to therapy module 315 todeliver pacing and/or shock and/or other arrhythmia preventiontherapies.

FIG. 7 is a block diagram illustrating generally, by way of example, butnot by way of limitation, one conceptual embodiment of portions ofpreventive therapy control module 355. In this embodiment, predictionscheduler 700 schedules predictions of future arrhythmias. Preventivetherapy decision module 705 decides whether arrhythmia preventiontherapy is warranted. Preventive therapy selection module 710 selectsone or more appropriate prevention therapies. Prevention activationmodule 715 activates the selected arrhythmia prevention therapy.Preventive therapy control module 355 also includes a prevention therapylist 720, and a trigger/marker vs. preventive therapy translation matrix725 that relates the prevention therapies of preventive therapy list 720to the triggers/markers used by arrhythmia prediction module 350 inpredicting future arrhythmias. The various submodules in therapy controlmodule 355 are illustrated as such for conceptual purposes only;alternatively, these submodules may be understood as being incorporatedin arrhythmia prediction module 350 or elsewhere.

In one embodiment, prevention therapy list 720 includes all the possiblearrhythmia prevention therapies that device 105 can deliver to thepatient. List 720 is programmed into device 105 either in hardware,firmware, or software. The members of list 720 may be selected based, byway of example, but not by way of limitation on: the patient's lead andelectrode configuration, the patient's dependence on pacing,functionality of the patient's AV node, etc. In one embodiment, eachmember of preventive therapy list 720 includes information fields (e.g.,pacing rate, interval sequences, etc.) that establish characteristics ofthat particular arrhythmia prevention therapy. In one embodiment,preventive therapy list 720 includes immediate, short-term,intermediate-term, and/or long term preventive therapies.

Immediate preventive therapies include, by way of example, but not byway of limitation: overdrive atrial pacing, such as for patients withfunctional AV nodes; demand atrial pacing, such as for patients withfunctional AV nodes; overdrive ventricular pacing; demand ventricularpacing; simultaneous or sequenced overdrive atrial and ventricularpacing; simultaneous or sequenced demand right ventricular and leftventricular overdrive pacing, such as for patients with a leftventricular pacing electrode; simultaneous or sequential rightventricular and left ventricular demand pacing, such as for patientswith a left ventricular pacing electrode.

Short-term preventive therapies include, by way of example, but not byway of limitation: repetitive overdrive atrial pacing, such as forpatients with functional AV nodes; repetitive simultaneous or sequenceddemand atrial and ventricular pacing; subthreshold cardiac stimulation,such as using defibrillation lead electrodes; neural stimulation of theautonomic nervous system, such as for patients with autonomicstimulation leads; global pacing pulses implemented by providing lowenergy shocks via defibrillation electrodes; delivery of certain drugs.

Intermediate-term preventive therapies include, by way of example, butnot by way of limitation, an alert/warning for the patient and/orphysician such as by providing an audible tone or other signal; deliveryof certain drugs.

Long-term preventive therapies include, by way of example, but not byway of limitation, diagnostic warnings for the physician, such as bytransmitting diagnostic information from device 105 to externalprogrammer 125 using a telemetry or other communication link; deliveryof certain drugs.

According to one aspect of the present system, each member of preventivetherapy list 720 is associated with a required time of action, whichincludes one or more of a time for the therapy to become effectiveand/or a time after which the therapy is no longer effective.Accordingly, in one embodiment, the prediction scheduler 700 considersonly those members of the preventive therapy list that can be expectedto be effective within a time frame commensurate with the predictiontime period. In other embodiments, information relating to the timeframe in which a particular therapy is expected to be effective isincorporated into the translation matrix 725, and the prediction timeperiod is added to the trigger/marker list 410. Other physiologicalvariables can also be added to the list 410 to form a kind ofphysiological state vector that can be mapped to a particular member ofthe preventive therapy list 720, with the translation matrixincorporating information relating to the appropriateness each member ofthe therapy list for a given value of a physiological variable.

In one embodiment, only one member of the preventive therapy list 720 isinvoked at any particular time. Combinations of different preventivetherapies are also provided, but each such combination is treated as aseparate entry in preventive therapy list 720. For example, “initiateoverdrive ventricular pacing” is one member in preventive therapy list720 while “initiate an alert/warning stimulus” is a different member inpreventive therapy list 720. A combined therapy using both “initiateoverdrive ventricular pacing” and “initiate an alert/warning stimulus”is treated as yet another entry in preventive therapy list 720. Thissimplifies the tasks of preventive therapy selection module 710 andprevention activation module 715.

Preventive therapy selection module 710 selects an arrhythmia preventiontherapy based on outputs from preventive therapy decision module 705. Ifpreventive therapy decision module 705 determines that the degree andconfidence in the arrhythmia prediction warrant some preventive therapy,as discussed above, then preventive therapy selection module 710 selectsa member of the preventive therapy list 720 to be invoked. In oneembodiment, the selection of a prevention therapy is based on the set oftrigger/marker detection values, D, upon which preventive therapydecision module 705 based the decision to provide arrhythmia preventiontherapy. Translation matrix 725 translates between the trigger/markerdetection values D and selection of the appropriate arrhythmiaprevention therapy from preventive therapy list 720.

FIG. 8 is a diagram illustrating generally, by way of example, but notby way of limitation, one embodiment of a translation matrix 725. Inthis embodiment, rows of translation matrix 725 correspond to themembers of trigger/marker list 410. Columns of translation matrix 725correspond to the members of preventive therapy list 720. In oneembodiment, the cells in translation matrix 725 are set to either “Use,”“Do Not Use,” or “Don't Care.” A “Use” entry indicates that thecorresponding preventive therapy is expected to provide a beneficialeffect to reduce the risk of the arrhythmia when the associatedtrigger/marker is present. A “Do Not Use” entry indicates that thecorresponding preventive therapy is expected to result in a detrimentaleffect that may increase the risk of arrhythmia when the associatedtrigger/marker is present. The “Don't Care,” entry indicates that thecorresponding preventive therapy is expected to neither increase ordecrease the risk of arrhythmia when the associated trigger/marker ispresent.

For the particular example illustrated in FIG. 8, when the first memberof trigger/marker list 410 is present, preventive therapies #1 and #6are likely to be useful, but preventive therapy #4 should not be usedbecause it is likely to be detrimental. The other possible preventivetherapies are deemed to have no effect when trigger/marker #1 ispresent.

In one embodiment, the values in translation matrix 725 arepopulation-based values that are initially loaded into device 105. Thesepopulation-based values are obtained for the population of interestbased on observations of: the occurrences of arrhythmias; the presenceof preceding triggers/markers; and the estimated or empirically derivedeffect of the particular preventive therapies on decreasing theprobability of arrhythmia.

In one embodiment, when preventive therapy decision module 705 indicatesthat a preventive therapy is needed, there will be some set of detectedtriggers/markers that initiated the decision to invoke arrhythmiaprevention therapy. Therapy selection module 710 considers those rows intranslation matrix 725 that correspond to those particulartriggers/markers that initiated arrhythmia prevention therapy. Selectionof the appropriate preventive therapy is based on a score determined foreach of the preventive therapies in preventive therapy list 720. Theparticular preventive therapy with the highest score is selected bypreventive therapy selection module 710.

In one embodiment, each member of preventive therapy list 720 isconsidered. For those rows that correspond to the presently detectedtriggers/markers, the number of “Use” entries is added to obtain thescore. If any “Do Not Use” entry is present, however, the score is setto zero. In this way, each of the preventive therapies has a scorebetween zero and the number of detected triggers/markers having acorresponding “Use” entry. Therapy selection module 710 selects thatpreventive therapy with the highest score. Where there is a tie, thepreventive therapy appearing earliest in preventive therapy list 720 isused. Where all possible preventive therapies have a score of zero, thennone of the available preventive therapies are deemed appropriate, andno preventive therapy is invoked. In one alternate embodiment, the scoreis also based on the conditional probability for each detectedtrigger/marker. In another alternate embodiment, a threshold value isincluded, such that the highest scoring preventive therapy must have ascore that reaches (or exceeds) the threshold value before anypreventive therapy is selected.

Prevention activation module 715 uses the information contained in thepreventive therapy list 720 and/or translation matrix 725 for activatingdelivery of the selected therapy by therapy module 315. Device 105delivers the activated therapy for a predetermined amount of time, whichis also included in the preventive therapy list 720. The arrhythmiaprevention therapy is delivered, by way of example, but not by way oflimitation, by controlling the pacing and/or shock circuits or bystoring information for later review (via telemetry or othercommunication to external programmer 125) by the physician or otheruser.

Example of Scheduling Predictions

Prediction scheduler 700 schedules the frequency with which arrhythmiapredictions are made. As discussed earlier, the BTP used to detect atrigger/marker for prediction may differ from the time period covered bya particular prediction which, in turn, may differ from the timerequired for the preventive therapy to have an effect. Predictionscheduler 700 should schedule predictions in a rational manner, forallowing efficient operation.

As described above, the arrhythmia probability statements can beconceptualized as having the following basic form: “In the next Y timeperiod there is an X percent chance of arrhythmia due to the combinationof triggers/markers D.” In one embodiment, predictions are made at amaximum of 2 minute intervals, and the BTP for detectingtriggers/markers is also 2 minutes. In an alternate embodiment, however,the prediction frequency may be different from the BTP frequency.Intervals between predictions may be longer or shorter than the BTP, andmay be variable or fixed. In one embodiment, the time period covered bythe predictions (i.e., the values for Y) are either 2 minutes (i.e., 1BTP), 30 minutes (i.e., 15 BTPs), 1 day, 1 week, or 1 month. Predictionscovering a 2 minute period are made at 2 minute intervals. Predictionscovering a 30 minute period are made at 10 minute intervals. Predictionscovering a 1 day period are made at 6 hour intervals. Predictionscovering a 1 week period are made daily. Predictions covering a 1 monthperiod are made weekly. Thus, in this embodiment, the predictionscovering shorter time periods are made at a frequency corresponding tothe time period covered by the prediction. Predictions covering longertime periods are made more often than the time period covered by theprediction. The timing of the predictions relative to the time coveredby the prediction could be made in many other ways without departingfrom the techniques disclosed in this document.

Preventive Therapy Decision

Preventive therapy decision module 705 decides whether preventivetherapy is warranted. In one embodiment, this task is performed at each2 minute interval (i.e., every BTP), at which time it considers allarrhythmia predictions existing at that time. In one embodiment, thetotal probability for arrhythmia for each of the prediction time periodsis compared to corresponding threshold values.

If the arrhythmia probabilities during all of covered time periods failto exceed the corresponding threshold values, then no significantarrhythmia risk is deemed to exist and no preventive therapy isprovided. If the arrhythmia probability during any of the covered timeperiods exceed the corresponding threshold value, then a significantarrhythmia risk is deemed to exist, and the arrhythmia preventiontherapy is selected and activated as described above.

Conclusion

Although the above description described particular embodiments using animplanted cardiac rhythm management device including defibrillationcapability, the techniques can also be used in other cardiac rhythmmanagement systems including, without limitation, implanted or externalpacemakers, or other acute or chronic cardiac care or monitoringdevices. Moreover, although the techniques were described using thoseelectrodes and sensors available in implanted cardiac rhythm managementdevices, different and/or additional sensing and/or stimulationelectrodes may be used (e.g., for sensing or stimulating sympathetic orparasympathetic nerves or ganglion, or for sensing pH, pO₂ or K+concentration in blood, etc.).

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the invention should, therefore, be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A cardiac rhythm management system, including: asensing module for detecting conditioning events statisticallyassociated with occurrences of arrhythmias; and, an arrhythmiaprediction module for predicting the occurrence of an arrhythmia withina specified prediction time period if an estimated arrhythmiaprobability exceeds a specified threshold value, wherein the estimatedarrhythmia probability is computed from a conditional arrhythmiaprobability associated with the conditioning event that is derived frompast observations of instances in which the conditioning event occur salone or together with an arrhythmia within a specified time period. 2.The system of claim 1 wherein the sensing module detects conditioningevents selected from a group consisting of a specific morphology of awaveform representing electrical activity of the heart, a specificpattern of activation times of different areas of the heart as sensed bya plurality of electrodes, a specific sequence pattern of heartbeatswith respect to time, a value of a measured physiological variable, anda statistic based upon a history of occurrences of conditioning events.3. The system of claim 1 wherein the sensing module detects a pluralityof conditioning events statistically associated with the occurrence ofan arrhythmia and further wherein the arrhythmia prediction modulecompares a composite estimated arrhythmia probability with the thresholdvalue in order to predict the occurrence of an arrhythmia, the compositearrhythmia probability being a combination of the estimated arrhythmiaprobabilities associated with each detected conditioning event.
 4. Thesystem of claim 3 wherein the conditional arrhythmia probabilities arebased upon past observations of the occurrences of events andarrhythmias taken from population data.
 5. The system of claim 3 furthercomprising an adaptive processing module for computing conditionalarrhythmia probabilities based upon past observations of the occurrencesof events and arrhythmias taken in real-time from a particular patient.6. The system of claim 5 wherein the conditional arrhythmiaprobabilities computed by the adaptive processing module are basedinitially upon past observations of the occurrences of events andarrhythmias taken from population data and each probability issubsequently updated from a previous value to a present value withobservations taken in real-time from a particular patient.
 7. The systemof claim 6 wherein the adaptive processing module tests the amount bywhich the present value differs from the previous value for statisticalsignificance before updating a conditional arrhythmia probability. 8.The system of claim 5 wherein the adaptive processing module tests thestatistical association of a conditional arrhythmia probability withreal-time observation data and discontinues use of a conditionalarrhythmia probability in deriving a composite conditional probabilityif the statistical association is below a specified value.
 9. The systemof claim 1 further comprising a therapy module for delivering apreventive arrhythmia therapy if the estimated arrhythmia probabilityexceeds a therapy threshold.
 10. The system of claim 9 furthercomprising a preventive therapy control module for selecting aparticular therapy to be delivered from a group of one or more availabletherapy modalities.
 11. The system of claim 10 wherein the group ofavailable therapy modalities includes at least one of: delivery of oneor more of a plurality of pharmacological agents, cardiac pacing in aselected pacing mode, delivery of cardioversion/defibrillation shocks,and neural stimulation.
 12. The system of claim 10 wherein the selectionof a particular therapy to be delivered by the therapy control module isbased upon ascertaining a physiologic state of the patient and decidingwhether or not a particular available modality of therapy is appropriatefor delivery to the patient in that physiologic state.
 13. The system ofclaim 12 wherein the physiologic state ascertained for selecting atherapy modality by the therapy control module includes at least one of:the prediction time period of the estimated arrhythmia probability, theparticular conditioning events used to make the arrhythmia prediction,the presence or not of specific conditioning events within a specifiedprior time period, and the magnitude of a measured physiologic variable.14. The system of claim 13 wherein the therapy control module decideswhether or not to deliver a particular mode of preventive therapy byperforming a matrix mapping of a list of detected conditioning events toa specific therapy or therapies considered most appropriate forpreventing an arrhythmia in that physiologic state, wherein the matrixmapping is performed using a translation matrix containing informationrelating to the appropriateness of a particular therapy modality for agiven detected conditioning event, and further wherein the mappingresults in a score for each therapy modality represented in the matrixthat is indicative of the appropriateness of each such therapy modalitygiven the list of detected conditioning events, the score beingevaluated by the therapy control module.
 15. The system of claim 13wherein the therapy control module decides whether or not to deliver aparticular mode of preventive therapy by performing matrix mapping of astate vector representing a specific physiologic state to a specifictherapy or therapies considered most appropriate for preventing anarrhythmia in that physiologic state, wherein the matrix mapping isperformed using a therapy matrix containing information relating to theappropriateness of a particular therapy modality for a given physiologicstate.